Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

Applying Classification Techniques in E-Learning System: An Overview

The aim of this paper is to provide an overview of application of data mining methods in e-learning process. E-learning is concerned with web-based learning which is totally depending upon internet. Use of data mining algorithms can help to discover the relevant information from database obtained from web based education system. This paper focused on e-learning problems to which data mining techniques have been applied, including: student’s classification based on their learning performance, detection of irregular learning behavior of students. This paper shows types of various modeling techniques used which includes: neural network, fuzzy logic, graph and trees, association rules and multi agent systems

Web Usage Mining for UUM Learning Care Using Association Rules

The enormous of information on the World Wide Web makes it obvious candidate for data mining research. Application of data mining techniques to the World Wide Web referred as Web mining where this term has been used in three distint ways; Web Content Mining, Web Structure Mining and Web Usage Mining. E-Learning is one of the Web based application where it will facing with large amount of data. In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. Web usage mining consists of three main phases, namely Data Preprocessing. Pattern Discovering and Patern Analysis. Main resources, server log files become a set of raw data where it's must go through with all the Web usage mining technique, Web usage mining approach has been combined with the basic Association Rules, Apriori Algorithm to optimize the content of the university E-Learning portal. Finally this paper will present an overview of results with the analysis and Web administrator can use the findings for the suitable valuable actions

Secure E-Learning by Using Data Mining Technique

There are many fields which are using data and technology approaches to improve Education and Learning.Academic Analytics is a supplier of high-quality, custom business intelligence data and solution. Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their factors, for reasons of understanding and optimizing learning and the environments in which it occurs .Data Mining techniques have been applied in both, Learning and Administrative problems. In Learning, the process is divided into learner-oriented and educator-oriented. In learner oriented the focus is on supporting the student to learn more effectively by suggesting new contents and in educator oriented the goal is to provide the educator a tool to allow system so it can guide the learner more effectively

Online Learning Management System and Analytics using Deep Learning

During this pandemic we have seen rise in popularity of online learning platforms. In this paper, we are going to discuss E-Learning using analytics and deep learning focusing on mainly four objectives which are login systems for teachers and students, Gamification to engage learners, AR contents to increase the involvement of learners and learning analytics to develop competency. We will use Data Mining and Buisness Intelligence to extract high level knowledge from the raw data of students. To predict engagement of students we have used several ML algorithms. This study provides an introduction to the technology of AR and E-Learning systems. The main focus of this paper is to use research on augmented reality and integrate it with Buisness Intelligence and Data Mining(DM). Engaging student till the end of the course became really difficult for traditional E-Learning Platform. Therefore, Gamification in E-learning is good way to solve this problem

E-learning by Time Dynamic Model Using Data Mining

The object of this paper is to build up Just in Time Dynamic Learner Models to analyze learners' behaviors and to evaluate learners' performance in online education systems by using rich data collected from e-learning systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to second patterns from which metrics can be derived from usage data. In this paper, we propose a six layer models(raw data layer, fact data layer, data mining layer, measurement layer, metrics layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, latter fact data from raw data, and then use clustering mining methods to create measurements and metrics